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[] Udemy - Autonomous Cars Deep Learning and Computer Vision in Python

  • 收录时间:2021-05-07 10:00:48
  • 文件大小:7GB
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  • 最近下载:2021-05-07 10:00:48
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文件列表

  1. 9. Artificial Neural Networks/10. Example 1 - Build Multi-layer perceptron for binary classification.mp4 384MB
  2. 8. Machine Learning Part 2/6. [Activity] Detecting Cars Using SVM - Part #2.mp4 204MB
  3. 11. Deep Learning and Tensorflow Part 2/8. [Activity] Build a CNN to Classify Traffic Siigns - part 2.mp4 175MB
  4. 6. Computer Vision Basics Part 3/11. Histogram of Oriented Gradients (HOG).mp4 169MB
  5. 4. Computer Vision Basics Part 1/9. [Activity] Convert RGB to HSV color spaces and mergesplit channels.mp4 167MB
  6. 9. Artificial Neural Networks/4. ANN Training and dataset split.mp4 151MB
  7. 11. Deep Learning and Tensorflow Part 2/7. [Activity] Build a CNN to Classify Traffic Signs.mp4 151MB
  8. 3. Python Crash Course [Optional]/7. Introduction to Seaborn.mp4 147MB
  9. 2. Introduction to Self-Driving Cars/1. A Brief History of Autonomous Vehicles.mp4 146MB
  10. 5. Computer Vision Basics Part 2/9. Hough transform theory.mp4 142MB
  11. 4. Computer Vision Basics Part 1/2. Humans vs. Computers Vision system.mp4 135MB
  12. 10. Deep Learning and Tensorflow Part 1/3. [Activity] Building a Logistic Classifier with Deep Learning and Keras.mp4 135MB
  13. 9. Artificial Neural Networks/1. Introduction What are Artificial Neural Networks and how do they learn.mp4 128MB
  14. 8. Machine Learning Part 2/5. Project Solution Detecting Cars Using SVM - Part #1.mp4 120MB
  15. 9. Artificial Neural Networks/2. Single Neuron Perceptron Model.mp4 120MB
  16. 4. Computer Vision Basics Part 1/1. What is computer vision and why is it important.mp4 119MB
  17. 5. Computer Vision Basics Part 2/11. Project Solution Hough transform to detect lane lines in an image.mp4 117MB
  18. 8. Machine Learning Part 2/7. [Activity] Project Solution Detecting Cars Using SVM - Part #3.mp4 117MB
  19. 4. Computer Vision Basics Part 1/8. Color Spaces.mp4 114MB
  20. 9. Artificial Neural Networks/6. Code to build a perceptron for binary classification.mp4 112MB
  21. 9. Artificial Neural Networks/8. Code to Train a perceptron for binary classification.mp4 110MB
  22. 7. Machine Learning Part 1/8. [Activity] Decision Trees In Action.mp4 104MB
  23. 9. Artificial Neural Networks/11. Example 2 - Build Multi-layer perceptron for binary classification.mp4 102MB
  24. 11. Deep Learning and Tensorflow Part 2/6. [Activity] Improving our CNN's Topology and with Max Pooling.mp4 102MB
  25. 5. Computer Vision Basics Part 2/2. [Activity] Code to perform rotation, translation and resizing.mp4 102MB
  26. 4. Computer Vision Basics Part 1/12. Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp4 99MB
  27. 4. Computer Vision Basics Part 1/3. what is an image and how is it digitally stored.mp4 99MB
  28. 7. Machine Learning Part 1/1. What is Machine Learning.mp4 96MB
  29. 7. Machine Learning Part 1/6. [Activity] Logistic Regression In Action.mp4 93MB
  30. 6. Computer Vision Basics Part 3/3. Template Matching - Find a Truck.mp4 90MB
  31. 5. Computer Vision Basics Part 2/5. Image cropping dilation and erosion.mp4 88MB
  32. 4. Computer Vision Basics Part 1/4. [Activity] View colored image and convert RGB to Gray.mp4 86MB
  33. 3. Python Crash Course [Optional]/5. Introduction to Pandas.mp4 86MB
  34. 3. Python Crash Course [Optional]/6. Introduction to MatPlotLib.mp4 85MB
  35. 9. Artificial Neural Networks/7. Backpropagation Training.mp4 84MB
  36. 4. Computer Vision Basics Part 1/10. Convolutions - Sharpening and Blurring.mp4 84MB
  37. 11. Deep Learning and Tensorflow Part 2/3. [Activity] Classifying Images with a Simple CNN, Part 1.mp4 84MB
  38. 5. Computer Vision Basics Part 2/8. [Activity] Code to define the region of interest.mp4 80MB
  39. 6. Computer Vision Basics Part 3/1. Image Features and their importance for object detection.mp4 79MB
  40. 8. Machine Learning Part 2/2. [Activity] Naive Bayes in Action.mp4 79MB
  41. 5. Computer Vision Basics Part 2/6. [Activity] Code to perform Image cropping dilation and erosion.mp4 77MB
  42. 6. Computer Vision Basics Part 3/5. Corner detection – Harris.mp4 77MB
  43. 8. Machine Learning Part 2/1. Bayes Theorem and Naive Bayes.mp4 76MB
  44. 5. Computer Vision Basics Part 2/10. [Activity] Hough transform – practical example in python.mp4 76MB
  45. 5. Computer Vision Basics Part 2/1. Image Transformation - Rotations, Translation and Resizing.mp4 76MB
  46. 1. Environment Setup and Installation/1. Introduction.mp4 75MB
  47. 10. Deep Learning and Tensorflow Part 1/1. Intro to Deep Learning and Tensorflow.mp4 75MB
  48. 8. Machine Learning Part 2/4. [Activity] Support Vector Classifiers in Action.mp4 74MB
  49. 9. Artificial Neural Networks/9. Two and Multi-layer Perceptron ANN.mp4 71MB
  50. 11. Deep Learning and Tensorflow Part 2/1. Convolutional Neural Networks (CNN's).mp4 71MB
  51. 5. Computer Vision Basics Part 2/4. [Activity] Perform non-affine image transformation on a traffic sign image.mp4 69MB
  52. 3. Python Crash Course [Optional]/1. Python Basics Whitespace, Imports, and Lists.mp4 68MB
  53. 4. Computer Vision Basics Part 1/14. [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam.mp4 68MB
  54. 9. Artificial Neural Networks/5. Practical Example - Vehicle Speed Determination.mp4 68MB
  55. 11. Deep Learning and Tensorflow Part 2/4. [Activity] Classifying Images with a Simple CNN, Part 2.mp4 67MB
  56. 4. Computer Vision Basics Part 1/11. [Activity] Convolutions - Sharpening and Blurring.mp4 66MB
  57. 7. Machine Learning Part 1/2. Evaluating Machine Learning Systems with Cross-Validation.mp4 66MB
  58. 10. Deep Learning and Tensorflow Part 1/2. Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding..mp4 64MB
  59. 4. Computer Vision Basics Part 1/7. What are the challenges of color selection technique.mp4 62MB
  60. 7. Machine Learning Part 1/7. Decision Trees and Random Forests.mp4 62MB
  61. 1. Environment Setup and Installation/2. Install Anaconda, OpenCV, Tensorflow, and the Course Materials.mp4 62MB
  62. 1. Environment Setup and Installation/3. Test your Environment with Real-Time Edge Detection in a Jupyter Notebook.mp4 61MB
  63. 6. Computer Vision Basics Part 3/12. [Activity] Code to perform HOG Feature extraction.mp4 61MB
  64. 5. Computer Vision Basics Part 2/3. Image Transformations – Perspective transform.mp4 60MB
  65. 6. Computer Vision Basics Part 3/7. Image Scaling – Pyramiding updown.mp4 58MB
  66. 6. Computer Vision Basics Part 3/6. [Activity] Code to perform corner detection.mp4 57MB
  67. 4. Computer Vision Basics Part 1/13. [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp4 53MB
  68. 5. Computer Vision Basics Part 2/7. Region of interest masking.mp4 52MB
  69. 4. Computer Vision Basics Part 1/5. [Activity] Detect lane lines in gray scale image.mp4 47MB
  70. 10. Deep Learning and Tensorflow Part 1/4. ReLU Activation, and Preventing Overfitting with Dropout Regularlization.mp4 44MB
  71. 9. Artificial Neural Networks/3. Activation Functions.mp4 43MB
  72. 6. Computer Vision Basics Part 3/2. [Activity] Find a truck in an image manually!.mp4 42MB
  73. 6. Computer Vision Basics Part 3/13. Feature Extraction - SIFT, SURF, FAST and ORB.mp4 42MB
  74. 11. Deep Learning and Tensorflow Part 2/2. Implementing CNN's in Keras.mp4 42MB
  75. 6. Computer Vision Basics Part 3/4. [Activity] Project Solution Find a Truck Using Template Matching.mp4 42MB
  76. 10. Deep Learning and Tensorflow Part 1/5. [Activity] Improving our Classifier with Dropout Regularization.mp4 41MB
  77. 7. Machine Learning Part 1/4. [Activity] Linear Regression in Action.mp4 41MB
  78. 6. Computer Vision Basics Part 3/10. [Activity] Code to obtain color histogram.mp4 40MB
  79. 8. Machine Learning Part 2/3. Support Vector Machines (SVM) and Support Vector Classifiers (SVC).mp4 40MB
  80. 7. Machine Learning Part 1/3. Linear Regression.mp4 36MB
  81. 4. Computer Vision Basics Part 1/6. [Activity] Detect lane lines in colored image.mp4 34MB
  82. 6. Computer Vision Basics Part 3/14. [Activity] FASTORB Feature Extraction in OpenCV.mp4 34MB
  83. 6. Computer Vision Basics Part 3/9. Histogram of colors.mp4 33MB
  84. 3. Python Crash Course [Optional]/2. Python Basics Tuples and Dictionaries.mp4 31MB
  85. 6. Computer Vision Basics Part 3/8. [Activity] Code to perform Image pyramiding.mp4 29MB
  86. 3. Python Crash Course [Optional]/3. Python Basics Functions and Boolean Operations.mp4 27MB
  87. 12. Wrapping Up/1. Bonus Lecture Keep Learning with Sundog Education.mp4 22MB
  88. 1. Environment Setup and Installation/4. Udemy 101 Getting the Most From This Course.mp4 20MB
  89. 3. Python Crash Course [Optional]/4. Python Basics Looping and an Exercise.mp4 19MB
  90. 2. Introduction to Self-Driving Cars/2. Course Overview and Learning Outcomes.mp4 15MB
  91. 7. Machine Learning Part 1/5. Logistic Regression.mp4 11MB
  92. 11. Deep Learning and Tensorflow Part 2/5. Max Pooling.mp4 8MB
  93. 9. Artificial Neural Networks/10. Example 1 - Build Multi-layer perceptron for binary classification.vtt 55KB
  94. 11. Deep Learning and Tensorflow Part 2/8. [Activity] Build a CNN to Classify Traffic Siigns - part 2.vtt 26KB
  95. 3. Python Crash Course [Optional]/7. Introduction to Seaborn.vtt 25KB
  96. 8. Machine Learning Part 2/6. [Activity] Detecting Cars Using SVM - Part #2.vtt 24KB
  97. 4. Computer Vision Basics Part 1/9. [Activity] Convert RGB to HSV color spaces and mergesplit channels.vtt 21KB
  98. 10. Deep Learning and Tensorflow Part 1/3. [Activity] Building a Logistic Classifier with Deep Learning and Keras.vtt 21KB
  99. 9. Artificial Neural Networks/4. ANN Training and dataset split.vtt 20KB
  100. 7. Machine Learning Part 1/8. [Activity] Decision Trees In Action.vtt 20KB
  101. 9. Artificial Neural Networks/2. Single Neuron Perceptron Model.vtt 19KB
  102. 3. Python Crash Course [Optional]/6. Introduction to MatPlotLib.vtt 19KB
  103. 5. Computer Vision Basics Part 2/9. Hough transform theory.vtt 19KB
  104. 6. Computer Vision Basics Part 3/11. Histogram of Oriented Gradients (HOG).vtt 18KB
  105. 9. Artificial Neural Networks/1. Introduction What are Artificial Neural Networks and how do they learn.vtt 18KB
  106. 3. Python Crash Course [Optional]/5. Introduction to Pandas.vtt 17KB
  107. 11. Deep Learning and Tensorflow Part 2/7. [Activity] Build a CNN to Classify Traffic Signs.vtt 17KB
  108. 2. Introduction to Self-Driving Cars/1. A Brief History of Autonomous Vehicles.vtt 17KB
  109. 5. Computer Vision Basics Part 2/11. Project Solution Hough transform to detect lane lines in an image.vtt 17KB
  110. 9. Artificial Neural Networks/8. Code to Train a perceptron for binary classification.vtt 16KB
  111. 10. Deep Learning and Tensorflow Part 1/2. Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding..vtt 16KB
  112. 3. Python Crash Course [Optional]/1. Python Basics Whitespace, Imports, and Lists.vtt 16KB
  113. 7. Machine Learning Part 1/2. Evaluating Machine Learning Systems with Cross-Validation.vtt 16KB
  114. 4. Computer Vision Basics Part 1/2. Humans vs. Computers Vision system.vtt 15KB
  115. 9. Artificial Neural Networks/6. Code to build a perceptron for binary classification.vtt 15KB
  116. 4. Computer Vision Basics Part 1/12. Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).vtt 15KB
  117. 7. Machine Learning Part 1/6. [Activity] Logistic Regression In Action.vtt 15KB
  118. 10. Deep Learning and Tensorflow Part 1/1. Intro to Deep Learning and Tensorflow.vtt 15KB
  119. 11. Deep Learning and Tensorflow Part 2/6. [Activity] Improving our CNN's Topology and with Max Pooling.vtt 15KB
  120. 8. Machine Learning Part 2/5. Project Solution Detecting Cars Using SVM - Part #1.vtt 14KB
  121. 4. Computer Vision Basics Part 1/8. Color Spaces.vtt 14KB
  122. 8. Machine Learning Part 2/1. Bayes Theorem and Naive Bayes.vtt 14KB
  123. 7. Machine Learning Part 1/1. What is Machine Learning.vtt 14KB
  124. 7. Machine Learning Part 1/7. Decision Trees and Random Forests.vtt 14KB
  125. 5. Computer Vision Basics Part 2/2. [Activity] Code to perform rotation, translation and resizing.vtt 14KB
  126. 8. Machine Learning Part 2/2. [Activity] Naive Bayes in Action.vtt 13KB
  127. 4. Computer Vision Basics Part 1/1. What is computer vision and why is it important.vtt 13KB
  128. 9. Artificial Neural Networks/11. Example 2 - Build Multi-layer perceptron for binary classification.vtt 13KB
  129. 4. Computer Vision Basics Part 1/4. [Activity] View colored image and convert RGB to Gray.vtt 13KB
  130. 8. Machine Learning Part 2/7. [Activity] Project Solution Detecting Cars Using SVM - Part #3.vtt 12KB
  131. 4. Computer Vision Basics Part 1/3. what is an image and how is it digitally stored.vtt 12KB
  132. 8. Machine Learning Part 2/4. [Activity] Support Vector Classifiers in Action.vtt 12KB
  133. 9. Artificial Neural Networks/7. Backpropagation Training.vtt 11KB
  134. 11. Deep Learning and Tensorflow Part 2/3. [Activity] Classifying Images with a Simple CNN, Part 1.vtt 11KB
  135. 11. Deep Learning and Tensorflow Part 2/4. [Activity] Classifying Images with a Simple CNN, Part 2.vtt 11KB
  136. 4. Computer Vision Basics Part 1/10. Convolutions - Sharpening and Blurring.vtt 11KB
  137. 5. Computer Vision Basics Part 2/8. [Activity] Code to define the region of interest.vtt 11KB
  138. 5. Computer Vision Basics Part 2/10. [Activity] Hough transform – practical example in python.vtt 10KB
  139. 9. Artificial Neural Networks/9. Two and Multi-layer Perceptron ANN.vtt 10KB
  140. 5. Computer Vision Basics Part 2/5. Image cropping dilation and erosion.vtt 10KB
  141. 11. Deep Learning and Tensorflow Part 2/1. Convolutional Neural Networks (CNN's).vtt 10KB
  142. 8. Machine Learning Part 2/3. Support Vector Machines (SVM) and Support Vector Classifiers (SVC).vtt 10KB
  143. 5. Computer Vision Basics Part 2/6. [Activity] Code to perform Image cropping dilation and erosion.vtt 9KB
  144. 10. Deep Learning and Tensorflow Part 1/4. ReLU Activation, and Preventing Overfitting with Dropout Regularlization.vtt 9KB
  145. 9. Artificial Neural Networks/5. Practical Example - Vehicle Speed Determination.vtt 9KB
  146. 5. Computer Vision Basics Part 2/4. [Activity] Perform non-affine image transformation on a traffic sign image.vtt 9KB
  147. 6. Computer Vision Basics Part 3/3. Template Matching - Find a Truck.vtt 9KB
  148. 7. Machine Learning Part 1/4. [Activity] Linear Regression in Action.vtt 9KB
  149. 11. Deep Learning and Tensorflow Part 2/2. Implementing CNN's in Keras.vtt 9KB
  150. 4. Computer Vision Basics Part 1/11. [Activity] Convolutions - Sharpening and Blurring.vtt 9KB
  151. 3. Python Crash Course [Optional]/2. Python Basics Tuples and Dictionaries.vtt 9KB
  152. 7. Machine Learning Part 1/3. Linear Regression.vtt 9KB
  153. 1. Environment Setup and Installation/3. Test your Environment with Real-Time Edge Detection in a Jupyter Notebook.vtt 9KB
  154. 5. Computer Vision Basics Part 2/1. Image Transformation - Rotations, Translation and Resizing.vtt 8KB
  155. 6. Computer Vision Basics Part 3/5. Corner detection – Harris.vtt 8KB
  156. 4. Computer Vision Basics Part 1/14. [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam.vtt 8KB
  157. 3. Python Crash Course [Optional]/3. Python Basics Functions and Boolean Operations.vtt 8KB
  158. 6. Computer Vision Basics Part 3/1. Image Features and their importance for object detection.vtt 8KB
  159. 3. Python Crash Course [Optional]/4. Python Basics Looping and an Exercise.vtt 7KB
  160. 4. Computer Vision Basics Part 1/5. [Activity] Detect lane lines in gray scale image.vtt 7KB
  161. 6. Computer Vision Basics Part 3/6. [Activity] Code to perform corner detection.vtt 7KB
  162. 1. Environment Setup and Installation/2. Install Anaconda, OpenCV, Tensorflow, and the Course Materials.vtt 7KB
  163. 5. Computer Vision Basics Part 2/7. Region of interest masking.vtt 7KB
  164. 5. Computer Vision Basics Part 2/3. Image Transformations – Perspective transform.vtt 7KB
  165. 4. Computer Vision Basics Part 1/13. [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).vtt 7KB
  166. 9. Artificial Neural Networks/3. Activation Functions.vtt 6KB
  167. 10. Deep Learning and Tensorflow Part 1/5. [Activity] Improving our Classifier with Dropout Regularization.vtt 6KB
  168. 6. Computer Vision Basics Part 3/12. [Activity] Code to perform HOG Feature extraction.vtt 6KB
  169. 4. Computer Vision Basics Part 1/7. What are the challenges of color selection technique.vtt 5KB
  170. 4. Computer Vision Basics Part 1/6. [Activity] Detect lane lines in colored image.vtt 5KB
  171. 6. Computer Vision Basics Part 3/7. Image Scaling – Pyramiding updown.vtt 5KB
  172. 6. Computer Vision Basics Part 3/10. [Activity] Code to obtain color histogram.vtt 5KB
  173. 6. Computer Vision Basics Part 3/4. [Activity] Project Solution Find a Truck Using Template Matching.vtt 5KB
  174. 2. Introduction to Self-Driving Cars/2. Course Overview and Learning Outcomes.vtt 5KB
  175. 6. Computer Vision Basics Part 3/2. [Activity] Find a truck in an image manually!.vtt 5KB
  176. 7. Machine Learning Part 1/5. Logistic Regression.vtt 5KB
  177. 1. Environment Setup and Installation/1. Introduction.vtt 4KB
  178. 6. Computer Vision Basics Part 3/13. Feature Extraction - SIFT, SURF, FAST and ORB.vtt 4KB
  179. 6. Computer Vision Basics Part 3/14. [Activity] FASTORB Feature Extraction in OpenCV.vtt 4KB
  180. 11. Deep Learning and Tensorflow Part 2/5. Max Pooling.vtt 4KB
  181. 1. Environment Setup and Installation/4. Udemy 101 Getting the Most From This Course.vtt 3KB
  182. 6. Computer Vision Basics Part 3/8. [Activity] Code to perform Image pyramiding.vtt 3KB
  183. 6. Computer Vision Basics Part 3/9. Histogram of colors.vtt 3KB
  184. 12. Wrapping Up/1. Bonus Lecture Keep Learning with Sundog Education.vtt 2KB
  185. 0. Websites you may like/[FCS Forum].url 133B
  186. 0. Websites you may like/[FreeCourseSite.com].url 127B
  187. 0. Websites you may like/[CourseClub.ME].url 122B
  188. 1. Environment Setup and Installation/2.1 Course materials page.html 102B